World-System (1960-2010) Controlling the Argentine Economy

 

Economists should probably admit that they don't know how to control the Economy. When an economist and politician such as Javier Milei gets elected as president of Argentina in 2023 and starts waving a chainsaw around as a symbol of cutting government, critics start to get nervous.

In a prior post (here) I found that Latin American Integration could stabilize the economy of Argentina. Unfortunately, Latin American Integration has been tried before and mostly failed, probably because Latin American has it's own problems with instability. The problem leaves me searching for other Geopolitical Alignments. In this post, I'll look more carefully at the ARL20 BAU model, that is, turning inward and confusing on Business as Usual.

First, why all the hand-wringing over Argentina? Millie has become the poster boy for the US Right Wing after giving a speech (with Elon Musk) at CPAC in 2025. The current Trump Administration and it's Department of Government Efficiency (DOGE), originally chaired by Elon Musk, seems intent on spying Milei's shock therapy. Unfortunately, or predictably, it seems that Milei's shock therapy has failed and will require a Bailout from the IMF and the US. So, it seems important to ask the general question about how (if at all) an unstable economy such as Argentina can be controlled?

First, the argument of Shock Therapy is that if we get the Government out of the economy, the Free-Market will take over and ensure prosperity. In other words, the free market will control the economy; if you have problems, it is because the market is not free of government interference. The "free market" assertion can be proven wrong (here).

From the standpoint of Systems Theory (where we have the best understanding of how to control systems), the first step is to establish an attractor path** among the competing Geopolitical models.

The attractor path (AP) for the ARL20 LAC Input model is presented above (dashed line) with the actual historical data (solid line). There are few deviations from the attractor path except for AR3 (the definitions for the state variables are given in the Measurement Matrix below in the Notes) around 1975 and after 2000. However, AR2 and AR3 are 

Environmental-Unemployment-Globalization 

controllers and should be relatively stable over time.

The attractor path (AP) for the ARL20 BAU model is presented above. Here, the period from 1980 onwards shows target departures for both historical feedback controllers. For AR2=(LU+EF+KOF-CO2), unemployment, Ecological Footprint (EF) and Globalization (KOF) departures were very large relative to Emissions (CO2). For AR3=(EF+HDI+CO2-KOF-LU), departures for Globalization (KOF) and Unemployment (LU) dominated. In the DCM model (see the Notes below), these two historical feedback controllers interact: shocking AR2 increases AR3=(EF+HDI+CO2-KOF-LU);  shocking AR3 decreases AR2=(LU+EF+KOF-CO2). In other words, Globalization, Unemployment and Environmental degradation are used as historical feedback mechanisms to control the Economy. 

The effects take decades to work out. The historical feedback controller coefficients are weak (the off-diagonal elements in the System matrix below). The feedback effects in the full ARL20 BAU model (including growth components) are also weak.

Exercise 1: Strengthen the feedback coefficients in the ARL20 BAU model and see if you can better control the system.

Controlling how the system responds to Unemployment, Globalization, Environmental degradation will be a great deal more challenging than cutting Government spending, but better system control is needed in Argentina and a free market will not accomplish everything that is needed (here) while Latin American Integration is a long way off in the future.

Notes

** The attractor path of a Dynamics Components State Space Model (DCM) can be computed with a free simulation starting from historical initial conditions (see Pasdirtz 2007). The free simulation that minimizes the AIC among competing Geopolitical models is considered the "best" attractor path, using some historical judgment when competing attractor paths are not well separated.

ARL20 model AIC summary:

ARL20 model Measurement Matrix AR1=(Overall Growth), AR2=(LU+EF+KOF-CO2), AR3=(EF+HDI+CO2-KOF-LU):

ARL20 model State Space Time Plot:

ARL20 BAU model Historical Feedback Controllers System Matrix:

ARL20 BAU model Historical Feedback Controllers Shock Decomposition: